FEATURE : AI
Managing data
AI requires data and models . The more data you feed it , the wider the visibility it can provide into the business . But the old saying garbage in , garbage out is certainly true of AI algorithms especially Large Language Models , LLMs – which can be seen as the pinnacle of AI development , but they are only one of many AI components .
THE ABILITY TO EMBED SMART ALGORITHMS EVERYWHERE IS NOT ONLY POSSIBLE , BUT ESSENTIAL IN ORDER FOR ORGANISATIONS TO COMPETE AND PROSPER .
They are a subset of generative AI , which is itself a subset of machine learning . The right AI tool will vary according to application . Large language models are revolutionising content management and transforming the ways in which we interact with unstructured content .
The latter refers to the process of extracting insights , patterns , and useful information from unstructured data sources which do not have a predefined model or format . This makes it more challenging to analyse when compared to structured data , which is organised in a specific format such as tables or databases .
Unstructured data comes in various forms , including text , social media posts , emails , audio recordings , images , videos , and more . These data sources often contain valuable information that can be utilised for decision-making , understanding customer sentiment , market analysis , fraud detection , and other business applications .
Unstructured data analytics involves applying techniques and technologies to extract meaningful information from such data and are considered a hidden gold mine for businesses . With their unprecedented ability to comprehend context , extract semantics and knowledge , and automate content management tasks , LLMs are unlocking new value in the content management system .
The emergence of generative AI and large language models has created a pivot point for the IT industry , where experimentation and differentiation can happen on a large scale . The ability to embed smart algorithms everywhere is not only possible , but essential in order for organisations to compete and prosper .
Let us start with operational data which are the transactional pieces , including ERP records and CRM data , such as payroll , supply chain , order entry , production data and development data . An effective AI solution will extract meaning and knowledge from this data which is real-time and typically not part of the training set of an LLM .
Then there is experience data which is information that is exchanged with customers , including elements that affect the experience of the customer , such as support . Learning data is a new form of data that represents the state of the AI algorithm and includes everything that has been learned from the models .
However , it should be noted that organisations may lack in-house LLM expertise and the right knowledge to get the most benefit from AI in order to construct learning data .
Cybersecurity
A recent research report estimated the global market for AI-based cybersecurity products was about $ 15 billion in 2021 and predicted to surge to approximately $ 135 billion by 2030 . Morgan Stanley reports that in the evolving landscape of artificial intelligence , AI , both cybersecurity teams and hackers are using AI to their advantage .
Security leaders must look to the adoption of technologies , strategies and skills that can enable managed , secure access . According to Forrester , simply blocking access is not a sustainable position , inevitably leading to many employees finding ways to bypass IT visibility and control .
Understanding and reacting to threats in real time is essential as is the choice of cybersecurity partner – you need one capable of tackling risks by applying AI-powered solutions with comprehensive monitoring capabilities .
Cybersecurity suppliers increasingly rely on AI in conjunction with more traditional tools such as antivirus protection , data-loss prevention , fraud detection , identity and access management , intrusion detection , risk management and other core security areas .
Morgan Stanley goes on to confirm that because of the nature of AI , which can analyse enormous sets of data and find patterns , it is uniquely suited to tasks such as :
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